两种群随机动力系统的信息熵和动力学研究

谢文贤; 蔡力; 岳晓乐; 雷佑铭; 徐伟

doi:10.7498/aps.61.170509

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摘要

随机种群动力学模型是研究种群间以及种群与不确定性环境间相互作用的动力学行为的数学模型. 本文从概率密度以及信息熵流、熵产生的演化角度探讨了两种群生态系统的Itô (或Statonovich)意义下随机模型的动力学行为.利用Fokker-Planck方程及其边界条件和信息熵定义导出信息熵流(平均散度)和熵产生的关系式,并通过数值路径积分法捕捉到熵流的非线性变化趋势以及信息熵的极值点位置与概率密度的快速迁移和分岔的联系. 应用数值路径积分法计算结果表明Itô (或Statonovich)意义下两种随机模型的概率密度和信息熵的极值点位置不同但演化趋势一致.

关键词:

作者及机构信息

1.
西北工业大学应用数学系, 西安 710072

基金项目: 国家自然科学基金(批准号: 11101333, 11102156, 10932009); 陕西省自然科学基金(批准号: 2011GQ1018)和西北工业大学基础研究基金资助的课题.

参考文献

[1]	Chen L S, Wang D D 1994 Phys. 23 408 (in Chinese) [陈兰荪, 王东达 1994 物理 23 408]
[2]	Gui Z J 2005 Models of Biological Dynamics and Computer Simulation (1st Ed.) (Beijing: Science Press) (in Chinese) [桂占吉 2005 生物动力学模型与计算机仿真 (第1版) (北京: 科学出版社)]
[3]	Chen L S, Meng X Z, Jiao J J 2009 Biological Dynamics (1st Ed.) (Beijing: Science Press) (in Chinese) [陈兰荪, 孟新柱, 焦建军 2009 生物动力学 (第1版) (北京: 科学出版社)]
[4]	Dimentberg M F 2002 Phys. Rev. E 65 036204
[5]	Pigolotti S, Flammini A, Maritan A 2004 Phys. Rev. E 70 011916
[6]	Cai G Q, Lin Y K 2004 Phys. Rev. E 70 041910
[7]	Cai G Q, Lin Y K 2007 Phys. Rev. E 76 041913
[8]	Wu Y, Zhu W Q 2008 Phys. Rev. E 77 041911
[9]	Cai G Q 2009 Int. J. Non-Linear Mech. 44 769
[10]	Cai G Q, Lin Y K 2011 Phys. Rev. E 76 041913
[11]	Nicolis G, Daems D 1998 Chaos 8 311
[12]	Daems D, Nicolis G 1999 Phys. Rev. E 59 4000
[13]	Bag B C, Chaudhuri J R, Ray D S 2000 J. Phys. A: Math. and General 33 8331
[14]	Bag B C, Banik S K, Ray D S 2001 Phys. Rev. E 64 026110
[15]	Bag B C 2002 Phys. Rev. E 66 026122
[16]	Xie W X, Xu W, Cai L, Jin Y F 2005 Chin. Phys. 14 1766
[17]	Bag B C 2003 J. Chem. Phys. 119 4988
[18]	Goswami G, Mukherjee B, Bag B C 2005 Chem. Phys. 312 47
[19]	Xie W X, Xu W, Cai L 2006 Acta Phys. Sin. 55 1639 (in Chinese) [谢文贤, 徐伟, 蔡力 2006 物理学报 55 1639]
[20]	Xu W, Xie W X, Cai L 2007 Phys. A-Stat. Mech. and its App. 384 273
[21]	Xie W X, Xu W, Cai L 2007 Chin. Phys. 16 42
[22]	Guo P Y, Xu W, Liu D 2009 Acta Phys. Sin. 58 5179 (in Chinese) [郭培荣, 徐伟, 刘迪 2009 物理学报 58 5179]
[23]	Guo Y F, Xu W, Li D X, Wang L 2010 Acta Phys. Sin. 59 2235 (in Chinese) [郭永峰, 徐伟, 李东喜, 王亮 2010 物理学报 59 2235]
[24]	Guo Y F, Xu W, Li D X 2009 Chin. J. Applied Mech. 26 264 [郭永峰, 徐伟, 李东喜 2009 应用力学学报 26 264]
[25]	Xie W X 2007 Ph. D. Dissertation (Xi'an: Northwestern Polytechnical University) (in Chinese) [谢文贤 2007 博士学位论文 (西安:西北工业大学)]
[26]	Zhang L P, Wang H N, Xu M 2011 Acta Phys. Sin. 60 010506 (in Chinese) [张丽萍, 王惠南, 徐敏 2011 物理学报 60 010506]
[27]	Gardiner C W 1985 Handbook of Stochastic Methods (Berlin: Springer-Verlag)

施引文献

[1]	Chen L S, Wang D D 1994 Phys. 23 408 (in Chinese) [陈兰荪, 王东达 1994 物理 23 408]
[2]	Gui Z J 2005 Models of Biological Dynamics and Computer Simulation (1st Ed.) (Beijing: Science Press) (in Chinese) [桂占吉 2005 生物动力学模型与计算机仿真 (第1版) (北京: 科学出版社)]
[3]	Chen L S, Meng X Z, Jiao J J 2009 Biological Dynamics (1st Ed.) (Beijing: Science Press) (in Chinese) [陈兰荪, 孟新柱, 焦建军 2009 生物动力学 (第1版) (北京: 科学出版社)]
[4]	Dimentberg M F 2002 Phys. Rev. E 65 036204
[5]	Pigolotti S, Flammini A, Maritan A 2004 Phys. Rev. E 70 011916
[6]	Cai G Q, Lin Y K 2004 Phys. Rev. E 70 041910
[7]	Cai G Q, Lin Y K 2007 Phys. Rev. E 76 041913
[8]	Wu Y, Zhu W Q 2008 Phys. Rev. E 77 041911
[9]	Cai G Q 2009 Int. J. Non-Linear Mech. 44 769
[10]	Cai G Q, Lin Y K 2011 Phys. Rev. E 76 041913
[11]	Nicolis G, Daems D 1998 Chaos 8 311
[12]	Daems D, Nicolis G 1999 Phys. Rev. E 59 4000
[13]	Bag B C, Chaudhuri J R, Ray D S 2000 J. Phys. A: Math. and General 33 8331
[14]	Bag B C, Banik S K, Ray D S 2001 Phys. Rev. E 64 026110
[15]	Bag B C 2002 Phys. Rev. E 66 026122
[16]	Xie W X, Xu W, Cai L, Jin Y F 2005 Chin. Phys. 14 1766
[17]	Bag B C 2003 J. Chem. Phys. 119 4988
[18]	Goswami G, Mukherjee B, Bag B C 2005 Chem. Phys. 312 47
[19]	Xie W X, Xu W, Cai L 2006 Acta Phys. Sin. 55 1639 (in Chinese) [谢文贤, 徐伟, 蔡力 2006 物理学报 55 1639]
[20]	Xu W, Xie W X, Cai L 2007 Phys. A-Stat. Mech. and its App. 384 273
[21]	Xie W X, Xu W, Cai L 2007 Chin. Phys. 16 42
[22]	Guo P Y, Xu W, Liu D 2009 Acta Phys. Sin. 58 5179 (in Chinese) [郭培荣, 徐伟, 刘迪 2009 物理学报 58 5179]
[23]	Guo Y F, Xu W, Li D X, Wang L 2010 Acta Phys. Sin. 59 2235 (in Chinese) [郭永峰, 徐伟, 李东喜, 王亮 2010 物理学报 59 2235]
[24]	Guo Y F, Xu W, Li D X 2009 Chin. J. Applied Mech. 26 264 [郭永峰, 徐伟, 李东喜 2009 应用力学学报 26 264]
[25]	Xie W X 2007 Ph. D. Dissertation (Xi'an: Northwestern Polytechnical University) (in Chinese) [谢文贤 2007 博士学位论文 (西安:西北工业大学)]
[26]	Zhang L P, Wang H N, Xu M 2011 Acta Phys. Sin. 60 010506 (in Chinese) [张丽萍, 王惠南, 徐敏 2011 物理学报 60 010506]
[27]	Gardiner C W 1985 Handbook of Stochastic Methods (Berlin: Springer-Verlag)

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两种群随机动力系统的信息熵和动力学研究

1. 西北工业大学应用数学系, 西安 710072

基金项目:

国家自然科学基金(批准号: 11101333, 11102156, 10932009)

陕西省自然科学基金(批准号: 2011GQ1018)和西北工业大学基础研究基金资助的课题.

收稿日期: 2011-12-20

修回日期: 2012-02-14

刊出日期: 2012-09-05

摘要: 随机种群动力学模型是研究种群间以及种群与不确定性环境间相互作用的动力学行为的数学模型. 本文从概率密度以及信息熵流、熵产生的演化角度探讨了两种群生态系统的Itô (或Statonovich)意义下随机模型的动力学行为.利用Fokker-Planck方程及其边界条件和信息熵定义导出信息熵流(平均散度)和熵产生的关系式,并通过数值路径积分法捕捉到熵流的非线性变化趋势以及信息熵的极值点位置与概率密度的快速迁移和分岔的联系. 应用数值路径积分法计算结果表明Itô (或Statonovich)意义下两种随机模型的概率密度和信息熵的极值点位置不同但演化趋势一致.

关键词:

Information entropies and dynamics in the stochastic ecosystem of two competing species

1.
Department of Applied Mathematics, Northwestern Polytechnical University, Xi'an 710072, China

Fund Project:

Project supported by the Natural Science Foundation of China (Grant Nos. 11101333, 11102156, 10932009), the National Natural Science Foundation of Shaanxi (Grant No. 2011GQ1018), and NPU Foundation for Fundamental Research.

Received Date: 20 December 2011

Accepted Date: 14 February 2012

Published Online: 05 September 2012

Abstract: Using the models of stochastic population dynamics, the competitions and interactions of interspecies and between species and the stochastic environment are studied. In this paper, the stochastic ecosystems (in Itô or Statonovich model) of two competing species are investigated through evaluating probability densities and information entropy fluxes and productions of two species. The formulas of entropy flux (i.e. expectation of divergence) and entropy production are educed for numerical calculations, through the corresponding Fokker-Planck equation with its condition and the definition of Shannon entropy. The nonlinear characteristics of entropy fluxes are captured and the relationships are found between the extremal points of entropy productions and the rapid transitions or bifurcations. The numerical results obtained with path integration method show that the probability densities and Shannon entropies of these two stochastic models (in Itô or Statonovich meaning) have the same evolutional tendency but with different points of extrema.

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